Targeted incentives based upon predicted behavior

ABSTRACT

A system and method for anticipating consumer behavior and determining transaction incentives for influencing consumer behavior comprises a computer system and associated database for determining cross time correlations between transaction behavior, for applying the function derived from the correlations to consumer records to predict future consumer behavior, and for deciding on transaction incentives to offer the consumers based upon their predicted behavior.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/626,003, filed Nov. 25, 2009, which is a division of U.S. applicationSer. No. 10/297,914, filed Jun. 13, 2003 now U.S. Pat. No. 7,653,594,which is a US national stage entry of PCT/US02/25957, filed Sep. 3,2002, which claims priority to U.S. provisional application No.60/365,546, filed Mar. 20, 2002. The entire contents of U.S. applicationSer. No. 12/626,003, filed Nov. 25, 2009, is incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to the field of marketing. More particularly, theinvention relates to targeted marketing.

2. Discussion of the Background

Marketing means communicating information regarding at least one ofproducts and services to consumers.

Targeted marketing means selectively marketing to a limited number ofconsumers, such as an individual, members of a family, persons with thesame residence as one another, or persons having some other piece ofrelevant information in common with one another. U.S. Pat. No. 5,832,457to O'Brien discloses targeted marketing.

Value means a good, a service, or a pecuniary interest including cash,check, credit, and conditional credit.

Transaction, in this application, means an exchange involving at leasttwo parties. A purchase is a transaction. Receipt of an incentive offer(such as the act of downloading an incentive offer from a web site),redemption of an incentive offer, and acceptance of participation in aconsumer survey are transactions.

Purchase, in this application, means a transaction in which cash, check,charge, or credit is exchanged for one or more goods and services.

Transaction incentive, in this application, means an offer for a certainvalue in exchange for entering into a specified transaction.

Purchase incentive, in this application, means an incentive wherein thespecified transaction for which the incentive is offered is a purchase.

Incentive, in this application, means the certain value associated withentering into a transaction specified in a transaction incentive.

Incentive terms and transaction incentive terms, in this application,each means the specifications of the transaction into which someone mustenter to be entitled to the incentive associated with the transactionincentive.

Predictive modeling is disclosed in U.S. Pat. No. 4,961,089 to Jamzadeh.Predictive modeling in this application means using data correspondingto prior events to determine a probability of occurrence of an eventduring some time period in the future. An example of a predictive modelwould be a formula whose inputs are the number of sun spots recordedover each of the last several years and whose output was a probabilityof a number of sunspots in the next year falling within a specifiedrange.

A prediction, in this application, means an assumption that one or moreevents, such as a transaction or a purchase, either are probable tooccur or are not probable to occur.

A class, in this application, means a set of things that share a commonattribute. An example of a class is the class of all things that aregoods. Goods are items of merchandise for sale. Another example of aclass is the class of goods that are packaged goods. Another example ofa class is the class of packaged goods that are canned goods. Anotherexample of a class is the class of all products that are sold under thesame brand name or trademark. Another example of a class is the class ofall products that are manufactured by the same manufacturer. Anotherexample of a class is the class of all goods that are diet related.Another example of a class is the class of all goods that areprescription drugs. Another example of a class is the class of allproducts that are breakfast products. Another example of a class is theclass of all service reservations that are rooming reservations, such asreservations at hotels and motels. Another example of a class is theclass of all services reservations that are transportation reservations,including car, bus, train, and airline reservations. A class may consistof items of a single product, such as items having the same UniversalProduct Code (“UPC”). For example, all product items having the UPC codeassociated with 12 ounce glass bottles of Coca Cola define a class.

A database is a collection of data. Typically a database is organized insome fashion so that information contained in the data may be readilyaccessed. In this application the term database means data organized insome format in a computer memory that can be accessed by an associatedcomputer system. Such a concept is also referred to as a databasemanagement system. A database or database management system includescommercial database products implemented in a computer system, such asthe Microsoft Access and SQL Server line of products as well as any setof files stored in computer memory that can be accessed by an associatedcomputer system.

Designated, as used in the foregoing example, refers to an associationof the data defining the good or service with data defining anattribute. All data defining products and services associated with thesame attribute are deemed to be in the same class defined by thatattribute.

Data defining a good or a service may be stored in a database. Such datamay be designated as a member of a class.

DISCLOSURE OF THE INVENTION Objects of the Invention

It is an object of the invention to provide to a consumer targetedincentives based upon a prediction whether the consumer will enter intoa transaction.

Generally speaking, it is an object of the invention to identifytransaction patterns of a set of consumers in an earlier time periodthat correlate to specified purchases by those consumers in later timeperiods.

More specifically, it is an object of the invention to identifytransaction patterns of a set of consumers in one or more earlier firsttime periods that correlate to purchases by those consumers of one ormore products and services within specified product and service classesin one or more later second time periods after the one or more earlierfirst time periods.

Generally speaking, it is an object of the invention to identifytransaction patterns of consumers in a first time period that correlateto changes in rate or amount of purchases within a specific product orservice class between the earlier first time period and the later secondtime period.

More specifically, it is an object of the invention to identifytransaction patterns of a set of consumers in one or more earlier firsttime periods that correlate to changes in purchases by those consumersof one or more products and services within specified product andservice classes between the one or more earlier first time periods andone or more later second time periods after the one or more earlierfirst time periods.

It is a further object of the invention to base a decision whether tooffer a consumer a transaction incentive, such as a purchase incentivefor a specified purchase within a certain product or service class, uponthe existence of the foregoing transaction patterns in the transactionhistory records of the consumer that correlate to that certain productor service class.

It is a still further object of this invention to determine from theforegoing correlations a probability or a prediction that the specifiedconsumer will purchase an item within a class of products or services.

It is still a further object of the invention to use the foregoingprobability or prediction that the specified consumer will or will notenter into a purchase transaction for an item within a class of productsor services as a factor in deciding what if any purchase incentives tooffer to that specified consumer.

It is a still further object of this invention to determine from theforegoing correlations relative likelihoods, for each member of a set ofconsumers, that the consumer will purchase an item within a class ofproducts or services, and to use that ranking in deciding whichconsumers to target market.

It is another object of this invention to perform targeted marketing bydepending offering to a consumer a transaction incentive upon aprobability that offering the incentive will change the consumer'spurchase behavior for purchase of an item specified in the purchaseincentive.

It is another object of this invention to perform targeted marketing bydepending the value of a purchase incentive, the preconditions for theconsumer to obtain the purchase incentive, or both, upon a quantitativedetermined probability or prediction of the consumer entering into apurchase transaction.

It is another object of the invention to perform targeted marketing soas maximize the expected return (either gross profit or net profit) tothe seller or manufacturer of an item of a product or service based uponthe cost of the incentive to the seller or manufacturer and a predictedprobability of changing the consumer's purchase behavior.

It is also a general object of the invention to minimize the costassociated with targeted marketing campaigns by increasing theredemption rate of incentive offers.

SUMMARY OF THE INVENTION

These and other objects of the invention are provided by a novelcomputer system programmed or designed to analyze consumer specific dataincluding historical consumer transaction data to identify statisticalcorrelations (correlation data) between transactions in classes indifferent time periods.

The same or a different computer system may use the correlation data incombination with consumer transaction data for a consumer to rank orestimate probability of that consumer entering into a transaction in aspecified class in a specified future time period.

The same or a different computer system may also be programmed to applythe rankings or probabilities to transaction incentive criteria todetermine whether to offer a transaction incentive associated with thecriteria to a consumer having the ranking or probability. The computersystem may determine the mechanism for making the offer of a transactionincentive, the timing of making the offer of a transaction incentive,the value associated with the offer of a transaction incentive, theclass or classes of transactions into which the consumer must enter inorder to receive the incentive.

The method of the invention provides predictions of future transactionsfor a consumer that are based upon applying a specified consumer'stransaction to one or more mathematical formula that define a likelihoodof a consumer entering into one or more transactions, and wherein themathematical formula incorporates correlation data obtained bystatistical analysis of transaction data for a set of consumers over atime period.

The correlations resulting from consumer transaction data may becombined with correlations to consumer purchase preferences from othersources, such as correlations from demographic data and customer loyaltyquotients. The correlations of a particular customer to these types ofdata may be incorporated into a ranking or probability function inpredicting the likelihood of a consumer entering into a specifiedtransaction in a specified future time period.

Some of the types of consumer behavior that may be predicted are changesin transaction behavior. For example the predictions may be that certainconsumers will be purchasing more or less in a specified class,switching brands in the same product class, purchase larger or smallervalues or quantities in a particular class, purchase a particular brandin a class when the consumer had not recently purchased in that class,or not purchase in a class the consumer previously purchased in(referred to herein as lapsing).

The particular class may be products and services associated with anyone or more designations. Some examples of classes of goods are freshmeat, canned goods, baking goods, diet foods, breads, cereals, coffees,dairy products, alcoholic beverages, beers, wines, and liquors. Moreexamples of classes are a specified brand of a type of product, such asKleenex brand tissues, Welch's brand grape juice. More examples ofclasses are a brand, per se, such as Kelloggs, and Green Giant. A classalso includes a group of brands, such as Keebler brands. Examples ofclasses include products, such as Green Giant eight ounce canned cornand Aunt Jemima one pound pancake mix. The particular class may be agroup of products having a feature in common, such as lower fatproducts, organic products, or products of one or more brands known tobe relatively low or relatively high priced.

The consumer transaction data may include data that are measures of anyof the following: purchases of a consumer (including transaction classdata and transaction date data), incentive redemptions by a consumer,reservations of a consumer, modality of offering transaction incentivesthat were redeemed by a consumer, frequency of transaction, dollarvolume of transaction, item count of purchase, frequency of redemption,value of redeemable incentive, and relative amount of incentivesredeemed in different classes. Preferably, all transaction data includesa date or a date range associated with each transaction.

The non-transactional data may include demographics, such as informationinvolving family size, number and age of children, age, and householdincome bracket. The non-transactional data may also include personalpreferences data provided by the consumer.

Customer loyalty quotient data is data indicating the fraction acustomer's expenses in classes sold at a specified retail store or chainof retail stores that the customer actually purchases at that retailstore or in the same chain of retail stores.

The method of the invention preferably uses transaction data from aplurality of distinct classes in determining correlations to a rankingor a probability of a consumer transaction in a specified class.

In certain instances, formula providing predictions of a consumerentering into a transaction in a specified class do not depend uponconsumer transaction data from that class, but only from consumertransaction data for transactions from other distinct classes. This isparticularly true when the formula is for a prediction that a consumer'spurchases in a specified class will lapse or that the consumer willpurchase in a class in which the consumer either has not previouslypurchased or has not purchased in a very long period of time, such as 2months, 4 months, or 6 months.

The results of the ranking, probabilities, and predictions may be usedas the basis of generating two generically different types of marketing.One type of marketing provides a transaction incentive that does notattempt to change the consumer's predicted transaction behavior. Forexample, an transaction incentive for purchasing in a class offered inresponse to a prediction that the consumer's purchasing in that classwill lapse attempts to change predicted behavior. A second type ofmarketing does not provide an incentive to modify the consumer'spredicted transaction behavior. For example, a transaction incentive forpurchasing a brand in a class when there is a prediction that theconsumer will purchase in that class does not attempt to changepredicted behavior. In addition, a subgeneric type of marketing does notattempt to change the consumer's predicted purchase behavior as to agenus class but does provide an incentive for the consumer to changepredicted transaction behavior in a species class of the genus. Forexample, a transaction incentive to purchase a second product brand in aspecified class when there is a prediction that the consumer willpurchase a first product brand in the specified class attempt to changepredicted behavior as to the species class but not the genus.

Data as to probability of a consumer redeeming a transaction incentivedepending upon the modality of the offer may be used to determinewhether to provide a transaction offer derived by using the method ofthe invention via a point of sale (POS) coupon, email, postal mail, settop box, or personal web site, and data as to the correlated time periodin which the prediction indicates consumer transaction behavior may beused to time the delivery of the transaction offer to the consumer.

In one aspect, the invention provides a system and method foranticipating consumer behavior and determining transaction incentivesfor influencing consumer behavior comprising a computer system andassociated database for determining cross time correlations betweentransaction behavior, for applying a function derived from thecorrelations to consumer records to predict future consumer behavior,and for deciding which transaction incentives to offer the consumersbased upon their predicted behavior.

In another aspect, the invention provides a system and method of its usecomprising a database, a computer system having read and write access tosaid database; and wherein the database stores a plurality of consumerrecords including a first consumer record for a first consumer, whereinsaid first consumer record stores (1) CID data (consumer identificationdata) indicating a first consumer CID for said first consumer; inassociation with said first consumer CID, at least (2) transaction datain a first transaction class field indicating items transacted by saidfirst consumer in a first transaction class during a first prior timeperiod and (3) predictive data in a first predictive field indicating atleast one of a ranking, a probability, and a prediction that said firstconsumer will transact in a first correlated class during a correlatedtime period, and wherein said correlated time period is subsequent intime to said prior time period. In additional aspects, said first classand said second class define the same class; said first class defines adifferent class than said second class; said first class is distinctfrom said second class; said first class is a genus and said secondclass is a species of said genus; said first class defines a species ofa genus and said second class defines said genus; said database stores,in association with said first consumer CID, transaction data in asecond transaction class field indicating items transacted by saidconsumer in a second transaction class during said first prior timeperiod; said database stores, in association with said first consumerCID, transaction data in a third transaction class field indicatingitems transacted by said consumer in a third transaction class duringsaid first prior time period; said database stores, in association withsaid first consumer CID, transaction data in a fourth transaction classfield indicating items transacted by said consumer in a fourthtransaction class during said first prior time period; said databasestores, in association with said first consumer CID, transaction data ina fifth transaction class field indicating items transacted by saidconsumer in a fifth transaction class during said second prior timeperiod; said database stores, in association with said first consumerCID, transaction data in a sixth transaction class field indicatingitems transacted by said consumer in a sixth transaction class duringsaid second prior time period; said database stores, in association withsaid first consumer CID, transaction data in a seventh transaction classfield indicating items transacted by said consumer in a seventhtransaction class during said second prior time period; said databasestores, in association with said first consumer CID, transaction data inan eighth transaction class field indicating items transacted by saidconsumer in an eighth transaction class during said second prior timeperiod; the system is programmed to decide whether to offer atransaction incentive to said first consumer based upon said predictivedata in said first predictive field for said first consumer; a term ofsaid transaction incentive is purchase in said first correlated class; aterm of said transaction incentive is purchase of a specified quantityin said first correlated class; a term of said transaction incentive ispurchase of a specified brand in said first correlated class; whereinterms of said transaction incentive are purchase in said firstcorrelated class during said correlated time period; wherein a term ofsaid transaction incentive is purchase in a class other than said firstcorrelated class; wherein a term of said transaction incentive ispurchase of a specified brand not in said first correlated class;wherein terms of said transaction incentive are purchases in a classother than said first correlated class during said correlated timeperiod; a terminal for presenting said transaction incentive to saidfirst consumer; a printer for printing said terms of transactionincentive; said printer is at a POS terminal; said printer is at aKiosk; said printer is at a consumer's computer; deciding whether tooffer a transaction incentive to said first consumer based upon saidpredictive data in said first predictive field for said first consumer;printing said transaction incentive; and printing in the presence ofsaid consumer.

In another aspect, the invention comprises a system and method for, in aset of customer records containing transaction data, correlatingtransactions in a first set of input classes in a first time period totransactions in a first correlated class in a second time period,wherein the second time period is subsequent to said first time period,thereby defining correlation data for said first set of classes; anddeciding whether to issue a transaction incentive to a customer based atleast in part upon transaction data for said customer in said first setof classes and said correlation data for said first set of classes.Additional aspects of the invention comprise correlating transactions ina second set of classes in said first time period to transactions in asecond correlated class in said second time period, thereby definingcorrelation data for said second set of classes; and deciding whether toissue a transaction incentive to a customer based at least in part upon(1) transaction data for said customer in said first set of classes andsaid correlation data for said first set of classes and (2) transactiondata for said customer in said second set of classes and saidcorrelation data for said second set of classes; correlatingtransactions in a second set of classes in a second time period totransactions in a second correlated class in said second time period,thereby defining correlation data for said second set of classes; anddeciding whether to issue a transaction incentive to a customer based atleast in part upon (1) transaction data for said customer in said firstset of classes and said correlation data for said first set of classesand (2) transaction data for said customer in said second set of classesand said correlation data for said second set of classes; a term of saidtransaction incentive is purchasing in said first correlated class; aterm of said transaction incentive is purchasing a specified brand insaid second correlated class; said transaction incentive is atransaction incentive for transacting in that one of said firstcorrelated class and said second correlated class having correlated datawith higher values; said deciding comprising determining whether saidtransaction data for said customer contains data indicating saidcustomer transacted in at least one class of said first set having arelatively high correlation values compared to other classes of saidfirst set; said at least one class comprises at least two classes; saidat least one class comprises at least three classes; said at least oneclass comprises at least four classes; said deciding occurs while saidcustomer is participating in a transaction at a POS terminal; offeringsaid transaction incentive to said customer while said customer isparticipating in said transaction and at said POS terminal; saiddeciding occurs between transactions of said customer; mailing ore-mailing said transaction incentive to said customer; offering saidtransaction incentive to said customer when said customer is identifiedat a terminal; said terminal is a POS terminal; said terminal is akiosk; said deciding whether to issue a transaction incentive is basedat least in part upon non transaction data for said consumer; said nontransaction data comprises demographic data; said non transaction datacomprises loyalty quotient data; and said deciding whether to issue atransaction incentive is based at least in part upon block data. CIDblock data is statistical data associated with customers in a localneighborhood region.

In yet another aspect, the invention provides a system and methodcomprising means for, in a set of customer records containingtransaction data, correlating transactions in a first set of inputclasses in a first time period to transactions in a first correlatedclass in a second time period, wherein the second time period issubsequent to said first time period, thereby defining correlation datafor said first set of classes; and means for deciding whether to issue atransaction incentive to a customer based at least in part upontransaction data for said customer in said first set of classes and saidcorrelation data for said first set of classes.

In yet another aspect, the invention provides a system and methodcomprising means for, in a set of customer records containingtransaction data, correlating transactions in a first set of inputclasses in a first time period to a change in transactions in a firstcorrelated class between said first time period and a second timeperiod, wherein the second time period is subsequent to said first timeperiod, thereby defining correlation data for said first set of classes;and deciding whether to issue a transaction incentive to a customerbased at least in part upon transaction data for said customer in saidfirst set of classes and said correlation data for said first set ofclasses. Additional aspects of the invention include correlatingtransactions in a second set of classes in said first time period to achange in transactions in a second correlated class between said firsttime period and said second time period, thereby defining correlationdata for said second set of classes; and deciding whether to issue atransaction incentive to a customer based at least in part upon (1)transaction data for said customer in said first set of classes and saidcorrelation data for said first set of classes and (2) transaction datafor said customer in said second set of classes and said correlationdata for said second set of classes.

In yet another aspect, the invention provides a system and methodcomprising means for (1) anticipating a consumer's behavior forpurchasing a first product based at least in part upon at least aportion of that part of said consumer's transaction history includingsaid consumer's transactions for products other than for purchase ofsaid first product; and (2) basing an incentive determination upon saidanticipating.

In yet another aspect, the invention provides a system and methodcomprising means for (1) anticipating a consumer's behavior forpurchasing a first product based at least in part upon at least aportion of that part of said consumer's transaction history includingsaid consumer's transactions for purchases of at least one product otherthan said first product; and (2) basing an incentive determination uponsaid anticipating. Additional aspects of the invention comprise thatsaid incentive determination is based at least in part upon differencesbetween said consumer's anticipated and prior purchase behavior for saidproduct; said differences are based at least in part upon variations infrequency of purchase; said differences are based at least in part uponvariations in quantity of purchase; said differences are based at leastin part upon variations in utilization of prior incentive for purchase;said differences are based at least in part upon variations in brandloyalty; said anticipating is based at least in part upon analysis ofdata in consumer records; and said anticipating is based at least inpart upon a model of consumer transaction behavior.

In yet another aspect, the invention provides a system and methodcomprising receiving transaction data at a POS terminal in a retailercomputer system; transmitting said transaction data over acommunications network to a central computer system; storing saidtransaction data in a database to which said central computer system hasread and write access;

-   -   (2) analyzing consumer transaction data stored in said        database; (3) determining transaction incentives to offer to        consumers associated with said transaction data; wherein said        central computer system is programmed to employ predictive        modeling on said transaction data to determine a predictive        modeling function, to determine at least one of rankings,        probabilities, and predictions of future consumer transactions        by applying consumer transaction data to said predictive        modeling function, and determining transaction incentives based        upon said at least one of rankings, probabilities, and        predictions. Additional aspects of the invention comprise        determining timing and method of transmission of said        transaction incentives that will most likely result in said        transaction incentives being redeemed; and transmitting said        transaction incentive over said communication network to said        retailer computer system.

BRIEF DESCRIPTION OF THE FIGURES

The invention is better illustrated in connection with the followingfigures.

FIG. 1 is a schematic of a computer network system;

FIG. 2 is high level flow chart;

FIG. 3A is an intermediate level flow chart of step 210 in FIG. 2;

FIG. 3B is an intermediate level flow chart of step 210 in FIG. 2;

FIG. 3C is an intermediate level flow chart of step 210 in FIG. 2;

FIG. 4 is an intermediate level flow chart of step 220 in FIG. 2;

FIG. 5 is another intermediate level flow chart of step 220 in FIG. 2;

FIG. 6 is a graph relating to probability outputs of the model;

FIG. 7 is a table illustrating a consumer record;

FIG. 8 is a table illustrating a consumer record with purchasesaccumulated by month;

FIG. 9 is a table illustrating cells containing fractions representingthe average for that cell in a set of records;

FIG. 10 is a table illustrating a cells containing fractionsrepresenting the averages for that cell in a set of records wherein allrecords have a certain value in the cell in row 5/00 and column P12; and

FIG. 11 is a table illustrating the difference between tables 9 and 10,which represents correlation data of each cell to the value of the cellin row 5/00 and column P12.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows a computer network system including network 10 (preferablythe Internet), analysis computer system 20 including server system 20 a,database 30, manufacturer computer system 40, retailer computer system50, consumer computer 60, databases 70, 80, 90, point of sale (POS)terminal 100, and kiosk 110. The lines connecting elements in FIG. 1indicate a means for data transmission including wire and wirelesstransmission hardware, data format, and transmission protocols. Eachcomputer system includes at least one digital computer includingassociated central processing unit, memory, input, and output devices.

Data stored preferably in database 30, but which may be stored in anyone of databases 30, 70, and 80 includes at least one of purchasetransactions data, redemption transactions data, reservationstransactions data, demographics data, and loyalty quotient data inassociation with a consumer identification.

Preferably, the transactions data for each transaction is stored inassociation with a date of the corresponding transaction. Redemptionstransaction data may be stored with more than one date: the date of thetransaction and the date or dates of the reservation. Redemptiontransactions data may be stored in association with more than one dateincluding the date of the redemption transaction and the data upon whichthe incentive transaction resulting in the redemption was offered.Demographics data and loyalty quotient data may also be stored inassociation with at least one date, such as the date upon which thedemographics data was received or the date on which the loyalty quotientwas calculated. Demographics data and loyalty quotient data may also bestored in association with another date or date range representing thedate range over with data resulting in the demographic data or loyaltyquotient data was received.

All data received in step 20 relating to a particular consumeridentification (CID) is stored in the database in association with thatCID. The database may be formatted as flat files or as one or morerelational database files and may include tables, forms, queries,relations, reports, modules, and other objects useful in databasemanagement and programming. For convenience, the data analysis will bedescribed below in the context of consumer records in which each recordincludes a field for CID (or CID in combination with store ID) and alarge number of associated fields. However, it will be apparent to thoseskilled in database programming that the relations between the data maybe otherwise than as specifically described, for example, by having datastored in third normal form.

The exemplary data format comprises a table with transaction records inwhich each record includes fields for CID, store ID, dates of purchase,identification of products, services, and reservations purchased inassociation with the date of purchase, identification of transactionincentives offered including terms of the offering in association withthe date of offering and dates of redemption of each transactionincentive. Each record may also include time of day of transaction, dayof week of transaction, day of month of transaction, form of payment(such as credit card, cash, check). Each transaction record may includea currency amount of each product item, transaction total currencyamount, currency amount of redemptions, incentive amount of offeredtransaction incentives, incentive amount associated with product item,frequent shopper ID, amount of purchase less discounts and incentives,manufacturer identifications associated with products for which eachincentive was offered or redeemed. Each transaction record may use auniversal product code (UPC) as the identifier of a product, service,reservation.

Preferably, there is one record per customer either per transaction, pertransaction date, or per date range.

The database may also store in association with a CID demographic dataincluding age, gender, income, household income, location of residence,location of work, postal code, family size, number of children, maritalstatus, gender, type of commuting vehicle, length of commute, type ofjob.

The database may also store in association with a CID block dataidentification. The database may also store in association with the CIDor the block data identification block data, which is data indicatingstatistical averages for transactions and other data for consumers thatlive in a specified block group.

The database may also store, in association with a CID, fields derivedfrom data, such as fields derived from demographic data for thecustomer, block data for the block to which the customer belongs, andloyalty quotient data for the customer's loyalty to a store, brand, orany class of transactions. These derived fields may contain datarepresenting likelihood of a consumer purchasing in a specified class,at a specified store, or in a store of a specified retail chain.

Derived fields also include fields for frequency of specified classes oftransactions, including purchases, redemption, and reservations, such asfor specified products, over a specified time period.

Preferably, analysis system 20 is either hard wired or programmed toperform the algorithms disclosed herein for predictive modeling, and theapplications of models to rank, determine probabilities, or makepredictions. However, retailer computer system 50 or manufacturercomputer system 40 may be programmed to perform those functions.

FIG. 2 shows step 200 of storing data, step 210 of predictive modeling,step 220 of determining incentive data and step 230 of offeringincentives.

In step 200 a database, such as database 30, receives and storesconsumer related data. Alternatively, any one of databases 70 and 80 mayreceive and stores consumer related data for performing the followingsteps.

In step 210, system 20 runs software or implements in hardware a form ofpredictive modeling to analyze a set of data for multiple CID records indatabase 30. The predictive modeling identifies correlations between theexistence of transactions in one or more classes of transactions in oneor more time period and the existence of transactions in one or moretransaction classes (hereinafter the correlated class) in at least onesubsequent time period (hereinafter the correlated time period). Thepredictive modeling may also include other correlations, such ascorrelations based upon a consumer's demographic, block data, andloyalty quotients.

Generally, speaking, the predictive modeling applies the statisticalcorrelations to any consumer's transaction data to predict whattransaction behavior that consumer is likely to enter into in thecorrelated time period. The model may generate a probability, aprediction, and it may rank the relative likelihoods of each member of aset of consumers entering into a class of transactions.

For example, in step 210, system 20 may determine the probability that aCID (that is, people or households associated with the CID) that had nopurchases of coffee associated with it will purchase coffee in the nextmonth. In step 210, system 20 may rank CIDs not previously associatedwith purchase of coffee according to their probability of purchasingcoffee in the next month. As another example, system 20 may rankhouseholds that had previously purchased coffee by their probability ofnot purchasing coffee in the next month (lapsing). As another example,in step 210, system 20 may determine the probability of a CID notpurchasing at a retail store in the next month.

In step 220, system 20 determines incentive data for CIDs, in which thedetermination is based at least in part upon the rankings generated instep 210.

In step 230, system 20 takes action that will ultimately provide thetransaction incentive to the consumer. This includes saving the data ina file for displaying or printing in human readable form, transmittingthe data to a POS computer system, an Internet web site, an emailaddress associated with the CID, or a postal mailing facility. Datadefining a transaction incentive for a consumer may be provided to theconsumer via email, at Kiosk 110 upon the consumer being identified bythe Kiosk, at a POS terminal when the consumer's CID is associated witha transaction at that terminal, or postal mailed to the consumer'spostal address associated with the CID.

FIG. 3A shows steps relating to step 210 that system 20 may implement indetermining a probability of a particular consumer changing a particularclass of transactions behavior in a specified class. The specified classand the correlated time period are represented by a specified datafield.

In step 310 a, system 20 receives input filter criteria. Input filtercriteria are criteria that are values for or depend upon statisticalcorrelations of input fields for data associated with consumers to thedata for the specified data field. The input fields are fields thatstore data for transactions occurring in one or more time periods priorto the correlated time period.

Examples of filter criteria depend upon the specific predictive model.However, an example of filter criteria is criteria specifying the 1, 3,5, or 10 fields most highly correlated to the specified data field.Another example of filter criteria is all fields having a value forcorrelation to the specified data field of greater than one of 0.6, 0.7,0.8, or 0.9. Another example of filter criteria is all fields having acorrelation to the specified data field of greater than 0.6 and whichare fields associated with transactions from a different class than thespecified class.

In step 320 a, system 20 applies the input filter criteria to a set ofrecords in order to determine which fields meet the criteria. The datafields that meet the criteria are the data fields used in subsequentsteps involving the transaction data. System 20 applies the input filterfunction using the input filter criteria to a set of data records fromdatabase 30 to define a subset of data fields in the records.Preferably, the set of records is large enough so that the results ofthe predictive modeling are statistically significant. This means thatat least about 50 records, preferably at least 100 records, andtypically 500 to 20,000 records are included in each predictivemodeling.

The input correlation filter criteria preferably includes at least onecross-correlation value between a field other than the specified datafield and the specified data field. A cross-correlation means a valuefor correlation between the specified data field and other data fieldsin the CID record, for a set of CID records. Number ofcross-correlations means a number, examples being 2, 4, 10, 20, 50, and100 of cross-correlations defined by different sets of two data fields,one of which is preferably the specified data field. The input filterfunction ranks the cross-correlations to the specified data field ofother data fields, and limits the input fields for step 330 a to thosedata fields most highly statistically correlated to the specified datafield.

The input filter function is applied only because current digitalcomputer systems are in practice not currently powerful enough toimplement the remaining steps of FIG. 2, given a very large number ofinput fields, such as the several thousand product specific transactionclasses defined by individual UPC codes. The filtering steps aretherefore optional and may not be practiced when sufficiently powerfuldigital computers become available. Moreover, instead of automatedfiltering, a user may select which fields to use for correlation in thefollowing step.

The specified data field is preferably a data field whose data indicatesquantity of transactions in the specified class in the correlated timeperiod. However, the specified data field may also be a field whose dataindicates a ratio of quantity of transactions in the specified class inthe later time period (correlated time period) to quantity oftransactions in the specified class in one of the earlier time periods.Quantity in this sense may be measured by number of items, volume ofitems, cost paid for items, or any other means to measure quantity.

In step 330 a, system 20 applies a predictive modeling algorithm to theinput data fields defined in the filtering step. The predictive modelingalgorithm is designed to result in a function that can be applied to aconsumer record to generate a value. The value obtained by applying thepredictive modeling algorithm to a record for a consumer is a rank,probability, or prediction that the actions of the consumer in thecorrelated time period will result in data in the specified field. Forexample, the predictive model algorithm may assume a predeterminedfunctional form, then determine cross correlations of the input datafields to the correlated data field, and then set coefficients in thepredetermined functional form to certain values based upon the values ofthe correlations that were obtained between the input data fields andthe specified data field.

The covariance, C, of two variables, A and B, is defined as the expectedvalue of their product E{AB} minus the product of their expected valuesE{A}E{B}. C=E {AB}-E{A}E{B}. The correlation coefficient r of thevariables A and B is by definition: r=C/sigma(A)sigma(B). Two variablesA and B are uncorrelated if their covariance is zero. The correlationcoefficient or the covariance may be used as the measure of thecorrelation between the target product or class and the input datafields.

The predictive model function is a function of the values of aconsumer's transactions in specified classes corresponding to the inputdata fields. Those classes are the classes which were the inputs to thepredictive modeling used to derive the correlations used in defining thepredictive model function for the specified class.

Preferably, the predictive model algorithm's predetermined functionalform also includes as input variables certain demographic variables,loyalty quotient variables, and block data variables. In thisalternative, the resulting predictive model function also depends uponthe data values in a consumer record for the corresponding certaindemographic fields, loyalty quotient fields, and block data fields. Inthis alternative, the value of the predictive model function reflectsthe likelihood of the consumer performing the specified transaction inthe correlated time period, including the impact on that likelihood dueto demographic data, loyalty quotient date, and block data in theconsumer's record.

In step 340 a, system 20 applies the predictive model function definedin step 330 a to a set of consumer data records. The predictive modelfunction generates data stored in association with each CID. Thegenerated data is preferably a ranking probability value, or prediction,Ps (predicting data), for the consumer transaction in class S for thatCID in the correlated time period. The predicting data may be stored toindicate probability or prediction of purchase in the specified claimsby the consumer having the specified CID in the correlated future timeperiod. The correlated time period is typically the next week, nextmonth, next six weeks, or next three month period compared to the mostrecent time period associated with data in the input data fields.However, the correlated time period may be a discontinuous time periodfrom the time period representing the transaction dates for data in thedatabase, such the following week, month, or quarter. The predictingdata may store data indicating a probability of reduction or increase inquantity of transaction in the specified class in the correlated timeperiod, such as the next week, month, or quarter.

Typically, one set of data records is used in steps 310 a to 330 a inorder to define the predictive model function. Then the predictive modelfunction is applied to a different set of records in step 340 a togenerate rankings, probabilities, and predictions for a correlated timeperiod corresponding to a future time period for use in making decisionsregarding transaction incentives to offer to the correspondingconsumers.

Alternatively, the predictive model function may also be applied to thedifferent set of consumer records in step 340 a to generate rankings,probabilities, and predictions for a correlated time period, but whereinthe correlated time period is for a historical time period in whichthere is actual transaction data in the set of records. In thisalternative, the rankings, probabilities, or predictions for thecorrelated time period may be compared to the actual transaction datafor the correlated time period to determine how closely the predictionsmatch actual data. This comparison may be used to generate a predictivemodel function validation value for the specific predictive modelfunction.

In step 350 a, system 20 may automatically compare the predictive modelfunction validation value to a predetermined value (such as the resultsof prior predictive model function validation values for modelspredicting the same specified data field) to determine whether to returnto step 310 a. If the system 30 returns to step 310 a, it automaticallyobtains a different set of input filter criteria based uponpredetermined programmed parameters and then repeats steps 320 a to 350a. Alternatively, the decisional step 350 a may be user controlled, inwhich case system 20 waits for user instructions once completing step340 a.

When decisional step 350 a does not loop back to step 310 a, processingproceeds to step 220 of FIG. 2. Typically, processing will only proceedto 220 when the correlated time period is a future time period.

Steps 340 a and 350 a enable feedback on the effect of the form or thecoefficients of the predictive model function. Thus, system 20 may storein code alternative functional forms which in addition to filtercriteria may change between iterations of cycle 310 a, 320 a, 330 a, 340a, and 350 a. System 20 may store a plurality of validation values inassociation with their predictive model functions for the specified datafield, and may store validations values in association with theirpredictive model functions for a plurality of specified data fields.Preferably, prior to proceeding to step 220, system 20 has determinedthe predictive model function providing the largest validation value foreach specified data field and calculated and stored the correspondingranking, probability or prediction data for each specified data field.

Preferably, a predictive model is generated using a relatively smallsubset of all CID records stored in a database, for computationalefficiency. However, the predictive model function or functions derivedfrom the algorithm of FIG. 3 a may subsequently be applied to any subsetor all CID records stored in database 30. Preferably, the predictivevalues, Ps, are determined for a large number of CID records in database30 and of course each Ps is stored in a field associated with thecorresponding CID.

FIGS. 3B and 3C also show slightly different algorithms corresponding tostep 210.

In step 310 b, system 20 (or a user) selects a target product or productclass. The specified product or class is one for which a prediction willbe generated as to the probability a consumer will change purchasebehavior. For example, the target could be a product class, such asbreakfast foods; a product type, such as cereal; a product brand, suchas Kellogg's cereal; a specific product, such as Kellogg's Corn Flakes;or a specific product size, such as a 16 ounce box of Kellogg's CornFlakes.

As in FIG. 3A, the transactions data could be any measure of theconsumer's purchase behavior, such as frequency of purchase, dollarvalue of purchase, item count of purchase, frequency of redemption ofincentives, value of redeemable incentive, relative amount of incentivesredeemed in different classes, price elasticity, or a measure ofresponsiveness to advertisements or marketing such as measure ofredemption of coupons, use of vouchers, or request for rebates.

In step 320 b, system 20 determines whether a correlation exists betweenpurchase data for a target product in a correlated later time period andthe other data in fields in a set of consumer transaction records indatabase 30 corresponding to transactions in an earlier time period.

System 20 may determine a correlation coefficient for a variable; A,that is a measure of behavior in a later time period for a specified ortarget product with purchase in an earlier time period of anon-specified product (or other recorded transaction activity).

System 20 may determine correlations of A with a set of variables Bi fori=1 to n where each Bi is a measure of the number of a non-targetproduct items or volume of purchase of a non-target product item in theearlier time period.

In step 330 b, system 20 selects a subset of variables, B, providingrelatively high correlations with the targeted product based on theresults of step 320 b. For example, assume system 20 is predicting whichhouseholds will be purchasing cereal during the following month. System20 identified a correlation between the target product of cereal andfive variables: B1, B2, B3, B4, and B5, which are measures of monthlypurchases of more than three gallons of milk, monthly purchases of atleast three loaves of bread, the presence of two or more children underthe age of 15 in the household, a monthly grocery bill exceeding 350dollars, and the absence of purchase of Kellogg's Pop Tarts during aone-month period, respectively. In step 330 b, system 20 selects thissubset of 5 variables to include in a predictive model. In step 330 b,system 20 defines a predictive model based upon the number of variablesselected and the correlations of each one of those variables with thetarget product. For example, the predictive model may set theprobability Ps that a consumer will purchase the target product in afuture time period to a normalized set of values for the suma1*B1+a2*B2+a3*B3+a4*B4+a5*B5 in which the weighting coefficients a1 toa5 in the linear combination are proportional to the correspondingcorrelation coefficients between the B data fields and the data for thespecified product in the correlated time period. Given this predictivemodel function, for example, if the target is the class of cereal,system 20 may predict a high probability of reduced purchase of cereal,or a low probability of purchase of cereal, for a CID record whichindicates purchases of more than three gallons of milk in the lastmonth, purchases of more than three loaves of bread in the last month,existence of three children in the household, household groceryexpenditures of at least $350 in the last month, and information thatthe household did not purchase Kellogg's Pop Tarts during the one-monthperiod. Depending upon the correlations and the resulting probabilityfunction, the high probability of reduced purchase of cereal, or a lowprobability of purchase of cereal could both be inverted.

In step 340 b, system 20 applies the probability function to the subsetof variables included in step 330 b in a probability function to obtaina value that will indicate either the probability that a consumer willpurchase the target product in a specified future time period or theprobability that the consumer will change purchase behavior for thetarget product.

In step 310 c, the target product or purchasing behavior that the modelis to predict is entered into system 20. That is, either a data field,set of data fields, or function of one or a set of data fields in CIDrecords is specified to system 20.

In step 320 c, system 20 examines a certain set of CID records indatabase 30.

In step 330 c, system 20 checks each record in the set to ascertainwhich consumer records show the presence of the predicted behavior. Ifthe predicted behavior is present in the customer record, system 20performs step 340 c. If the consumer record does not meet the predictedbehavior, system 20 performs step 360 c.

In step 340 c, if the consumer record meets the predicted behavior,system 20 adds the consumer record to a target matching database table.

In step 350 c, system 20 checks to see if there are additional consumerrecords to access. If there are additional consumer records to access,system 20 performs step 320 c. If there are no more consumer records toaccess, system 20 performs step 365 c.

In step 360 c, the consumer record does not meet the predicted behaviorand system 20 adds the consumer record to a non-target matching table indatabase 30.

In step 365 c, system 20 determines correlations between the targetedpurchasing behavior and the non-targeted data fields. The correlationsare discernable by comparing the mean value for each non-targetedproduct in the target matching database to the mean value for thatnon-targeted product in the non-target matching database. A significantdifference in the mean values indicates that a correlation existsbetween non-targeted data field and the targeted purchasing behavior.The larger the difference in mean values, the larger the correlation.System 20 can define a predictive model as discussed for FIGS. 3A and 3Bbased upon the values of the correlations between the non-targetedproducts and targeted purchasing behavior.

In step 370 c, system 20 reads a customer record from database 30 andapplies the predictive model to determine probability of the targetedpurchasing behavior occurring.

In one embodiment, the generation and storing of ranking, probability,or prediction criteria is bypassed. In this embodiment, incentivedecisions are made by determining whether certain data exists in thecustomer's transaction record, wherein the certain data are in, fieldsfound to be highly correlated to the specified class or specifiedtransaction data. In this embodiment, a decision whether to offer atransaction incentive for a transaction in the correlated time period inthe specified class depends upon the existence of the predeterminedpattern of transactions found to be correlated to the specified datafield. This pattern matching based decisional process has the advantageof avoiding the complexity of storing intermediate data, namely theranking, probability, or prediction data for each customer.

FIG. 4 shows steps relating to one example of step 220, determiningtransaction incentives from predictions obtained from a predictive modelfunction. In step 410, system 20 determines whether the probability thata consumer (i.e., a consumer or household associated with a CID) who haspurchased W will cease purchasing W (indicated by the subscript “−W”)and decides based upon that probability whether to offer an incentive tothat consumer for purchase of W. As shown, if the probability of theconsumer changing his or her purchase behavior to stop purchasing W isgreater than or equal to 0.8, system 20 decides to implement step 420and provide an incentive on purchase of W to the consumer. The value ofthe incentive may depend upon the value of the probability. If theprobability is less than 0.8, system 20 implements step 430 and decidesnot to offer the consumer an incentive for purchase of W.

FIG. 5 also shows steps relating to step 220. In step 510, system 20determines the probability that a consumer (i.e., a consumer orhousehold associated with a CID) who has purchased W will ceasepurchasing W, and decides based upon that probability whether to offeran incentive to that consumer for purchase on a product correlated to W.As shown, if the probability of the consumer changing his or herpurchase behavior to stop purchasing W is greater than or equal to 0.8,system 20 decides to implement step 520 and provide an incentive forpurchase on a product correlated to W to the consumer. The value of theincentive may depend upon the value of the probability. If theprobability is less than 0.8, system 20 implements step 530 and decidesnot to offer the consumer an incentive for the purchase of W.

FIG. 6 shows CID records ordered by probability, from highest to lowestprobability extending along the X axis. FIG. 6 shows the percent of CIDs(from 0%, 10%, 50%, 100%) on the X axis having the probability shown onthe Y axis. Here, probability means the probability for a CID havingdata in the specified data field.

In optional step 350 a, filter criteria are changed, and the process ofsteps 310 a-340 a are repeated based upon the new criteria. The changemay be programmatically implemented based upon programmed criteria ormanually implemented by commands entered by a user via an input/outputdevice.

FIG. 6 shows a ranking of consumers by probability of consumers changingfrom previously purchasing product W to not purchasing product W(indicated by “−W”). As shown, 10% of CIDs have a probability of greaterthan 0.7. These consumers may be ideal candidates for an incentive. Theconsumers whose CIDs are represented on the X axis by the 50% to 100%range are shown having probabilities of changing purchase behavior on Wof less than about 0.4. This means that one half of all consumers havingCID records in the system are determined to have less than about 0.4probability of ceasing their purchases of W.

FIGS. 4-6 relate to predicting the probability of a consumer changingbehavior from purchasing a product in one time period to not purchasingthat product in a subsequent time period. In those cases, one marketingpossibility is to target market consumers having a high suchprobability. Alternatively, the system and method disclosed herein maybe used to determine consumers that have not previously purchased W whowill start purchasing W. Decisions on incentives to those consumers forW and for products correlated to purchase of W may be made dependingupon this probability.

System 20 could decide whether to issue an incentive offer based solelyon a score generated by the model for a target product. The system couldalso decide to issue different incentive offers based on score. Examplesof different incentive offers include free target products; discounts onthe target product, offers on a different brand in the same class as thetarget product, offers on a product complimentary to the target product;and discounts on purchases of multiple packages the target product.Complementary products are those products often used on conjunction withone another, such as milk and cereal, or cheese and crackers, orreservations for flights and reservations for hotels.

In addition, an incentive program could be part of a marketing campaignor as a method of generating further information on the purchasinghabits of consumers to help improve accuracy of system 20's predictivemodeling.

In addition to recognizing the presence or absence of a product, system20 may be programmed to account for the consumer's changes in purchaselevel for specific products. For example, a consumer purchased eightgallons of milk in February/March but only four gallons in April/May.System 20 could use milk purchase data for both prior time periods toaccount for tendencies that are common to the population as a whole. Forexample, people may not drink as much milk when it is hot outside.System 20 could use milk purchase data to account for tendencies thatare specific to the consumer. For example, system 20 may determine fromrecords based on at least one of the consumer's airline tickets, hotelrecords, and grocery purchase records from a location distant from thecustomer's normal place of purchase that the consumer had been out oftown for a certain time period.

Detailed examples of algorithms for performing the invention follow inorder to explain aspects of the invention and identify types oftransaction incentives applicable to the results of the predictivemodeling.

First, obtain a consumer transaction database containing transactiondata for a first set of consumers. This transaction database may alsoinclude demographic data. In this example, the database containstransactions for classes P1, P12. P1, . . . P12 may each represent theproducts having the same Universal Product Code (UPC), or any class ofproducts sharing a common attribute as previously discussed.

Second, cumulate the transactions records for each consumer by timeperiod, such as by month. That is, identify the number of item orservices purchased in association with a consumer identification withineach one of classes P1, . . . P12, in each month. Tables 1 and 2illustrate this step.

In FIG. 7, table 1 shows a customer's transactions in association withdates of transaction for transaction classes P1 to P12. Each row intable 1 represents a transaction and specifies the date of thetransaction and the quantity of items transacted in each class.

In FIG. 8, table 2 shows the cumulation of quantity of items transactedin classes P1 to P12 by time period. More specifically, table 2 showsthe cumulation of transaction data for customer 1 in each one of monthsof January through May of the year 2000 (1/00 to 5/00). Alternatively,place a 1 in the cells in table 2 for any month and Pi in which theconsumer has purchased at least one item.

Second, for each class Pi and each one of the time periods, calculatethe fraction of the consumer records of the first set of consumershaving a non-zero value.

In FIG. 9, table 3 graphically illustrates the result of the secondstep.

Third, identify a first subset of cumulated transaction history recordsfor consumers of the first set that purchased at least 1 item in classP12 in time period 5/00. That is, filter from the larger set of recordsa sub set in which the fraction for P12 is 1. That is, include in thefirst subset only customer transaction records that have a non-zerovalue for transactions of class P12 in time period 5/00.

Fourth, repeat the second step on the first subset. That is, for eachclass Pi and each one of the time periods in the first subset, calculatethe fraction of consumer records having a non-zero value.

In FIG. 10, table 4 graphically illustrates the result of the fourthstep.

Fifth, subtract the values in cells in Table 3 from the values in thecorresponding cell in Table 4.

In FIG. 11, table 5 illustrates the result of the fifth step. Table 5shows values in each cell that are representative of correlations to theexistence of transactions in P12 in time period 5/00. A positive valuein table 5 indicates purchase in the corresponding class correlates topurchase of P12 in time 5/00, with the magnitude of the value indicatingthe degree of correlation. A negative value in Table 5 indicates failureto purchase in the corresponding class predicts purchase of P12 in time5/00, with magnitude of the value indicating the degree of correlation.

Alternatively, instead of correlating to a fraction representing theexistence or lack of existence of a transaction in each cell in table 5,steps 1 to 4 could have been modified so that table 5 correlated to theaverage number of items purchased. That is, tables 3 and 4 could havebeen determined by calculating the average number of items transactedinstead of the fraction of the consumer records having non-zero values.In that case, the cells in table 5 would contain numbers indicating anaverage difference in number of items purchased for those in the firstsubset compared to the average number of items purchased for those inthe first set. Use of data in this alternative would require subtractingthe average values determined for the first set of consumers from theactual transaction data for a specific consumer, and then determiningwhether the result of that subtraction correlated to the alternativetable 5 data. This additional step is not necessary when using thecorrelation data shown in table 5, as explained below.

The correlation data shown in table 5 may be used in many ways toestimate the likelihood or relative likelihood of a consumer transactingin class P12 in the future. Preferably, the correlation data is used byoperating with it on an individual consumer transaction record todetermine a likelihood of that consumer subsequently purchasing in P12in the correlated time period, or a relative likelihood relative toother consumers, of that consumer subsequently purchasing P12 in thecorrelated time period.

A consumer having non-zero transactions in a class and a time periodthat is correlated to a future transaction in class P12 is a predictorthat the consumer will subsequently conduct a transaction in P12. Thatprediction may be used as one factor in deciding whether and when toprovide to that particular consumer a transaction incentive offer topurchase in class P12 in the correlated time period. A consumer havingno transactions in any class and a time period that is correlated to afuture transaction in class P12 is a predictor that the consumer willnot subsequently conduct a transaction in P12 in the correlated timeperiod. That prediction may be one factor used in deciding whether andwhen to offer that particular consumer an incentive to purchase in classP12. Moreover, correlations or lack thereof may be used to determine thevalue of any incentive transaction to offer to the consumer.

The identification of correlations and their magnitude indicated bynon-zero values in table 5 may be used to combine the significance ofsome or all of the correlations. One method involves determine thedifference in the fraction of consumers that purchase in two or more ofthe correlated classes identified in table 5 and subsequently purchasein class P12 in time period 5/00 (the correlated time period) to thefraction of consumers that purchase those two correlated productswithout regard to purchase of P12 in time period 5/00. Similarly,fractional difference correlations may easily be calculated for anycombination of cells of table 5 showing a correlation. More than onesuch correlation value for two or more of the cells shown in table 5 maybe calculated. The resulting correlations may be used individually orcombined in a ranking function as discussed in the next paragraph.

Alternatively, we can derive a ranking function by defining a multivariable correlation formula applicable to the data for a consumer'stransactions that cumulates the significance of each of the single classcorrelations shown in table 5. Applying the derived multi variablecorrelation formula to a second set of consumer transaction recordsresults in values that rank each consumer record by the likelihood ofthe corresponding consumer purchasing in P12 in the correlated timeperiod. An expression for an exemplary ranking function follows. Definea ranking function R(P12, n, Sk)=R, which defines a rank R for consumerrecord Sk's likelihood of transacting in class P12 in time period n. Wedefine R(P12, n, Sk)=Σ(NCij if Cij is positive −Cij if Cij is negative),where Nij represents the number of items transacted in class i in timeperiod j, Σ represents summation over values of i and j, Cij are thecorrelation values derived as indicated for table 5, and j runs from 1to 12, or 1 to 11. In the example represented by table 5, non-zerovalues of Cij only exist for certain i values wherein j=n−1 (time period4/00) or j=n−2 (time period 3/00). However, we recognize thatcorrelations between more than three time periods, and non-consecutivetime periods may exist, and the general form for R(P12, n, Sk) accountsfor these possibilities.

Any of the multi variable correlation functions applied to a set ofconsumer transaction records will result in a distribution of values forconsumer records Sk. The records of some consumers will have relativelyhigh R values and the records of other consumers will have relativelylow R values. Relatively high values indicate a relative likelihood thatthe corresponding consumers will purchase in category P12 in thepredicted time period. The system of the invention may use bothrelatively high values and anticipated time period of purchase andrelatively low values as inputs in determining whether to make availableto a consumer an incentive offer and when to make the incentive offeravailable.

Statistical analysis, heuristic, or ad hoc rules may be applied to the Rfunction to map R values to probabilities in the range 0 to 1. Thesimplest method of mapping the R values for a set of consumers to aprobability range is to normalize the R values by dividing all of the Rvalues for a set of consumers by the largest R value in the set.

The results of the ranking or probability of purchase of P12 in timeperiod 5/00, the correlated time period, may be used in decisionsregarding offering of incentives in a variety of ways.

In the case of a relatively high likelihood of a consumer purchasing inclass P12 in a correlated time period, one of the manufacturerscompeting for sales in class P12 may decide to offer the consumer adiscount on their brand product in class P12, and at a time, such as aday a few days, or a week prior to the anticipated purchase time period,or during the anticipated purchase time period. If the consumer's priorpurchase data shows that the consumer preferentially purchases thatmanufacturer's products in that class P12, the manufacturer may decideto offer no incentive or to offer only a low value incentive. If theconsumer's prior purchase data shows that the consumer preferentiallypurchases a different manufacturer's product in class P12, themanufacturer may decide to offer a large value incentive in an attemptto induce the consumer to try its product. If the consumer's priorpurchase data shows that the consumer has purchased from a variety ofdifferent manufacturer's product in class P12, the manufacturer maydecide to offer an intermediate value purchase incentive in an attemptto induce the consumer to try its product. The determinations based uponconsumer's preference to a different manufacturer in class P12 is anexample of a class and subclass determination. The class is P12. Thesubclass is the brand of the manufacturer of products in P12 to whichthe customer's prior sales are associated.

One benefit of this invention is that it enables manufacturers andretailers to make determinations regarding incentive determinationsbased upon a ranking or likelihood of a consumer purchase in a class andadditional data regarding the consumer's preferences or purchase historyin that class or a subclass of that class. Thus, the invention providesfor making decisions regarding purchase incentive offers based upon acustomer's prior purchase history in (1) a class and (2) a subclass ofthat class and correlations to either the class or the subclassindicating likelihood of the consumer purchasing in either the class orthe subclass in a correlation time period.

In the case of a relatively low likelihood of a consumer purchasing inclass P12 in a correlated time period, a manufacturer may decide eitherto offer the consumer a relatively high value purchase incentive forpurchasing in P12 or to forego offering the consumer a purchaseincentive in P12.

The foregoing references to manufacturers in this example apply equallyto retailers. Especially when those retailers (1) desire to increasesales by offering purchase incentives on products a consumer is unlikelyto otherwise purchase, (2) introduce their own brands in their stores(house label brands) in competition with the other brands they sell intheir stores, and (3) desire to motivate consumers to perform subsequentshopping transactions in stores of the same retailer rather than instores of different retailer, such as a competing retailer.

One advantage of the invention is that it enables a determination of acorrelation time period when a consumer is unlikely to purchase in aspecified class, even when the consumer's purchase history showstransactions in that class in the past. This enables manufacturers andretailers to avoid the expense of generating and transmitting toconsumers incentive offers that have little likelihood of being used.

The ranking and probability determinations identified above for P12 canbe repeated for each one of classes P1 to P11. Manufacturers andretailers may make decisions regarding incentive offers for a consumerbased upon all of that information. For example, a manufacturer maydecide to offer an incentive to a consumer only for the one class inwhich it is most likely that the consumer will purchase in the class,and will likely purchase from another manufacturer. Alternatively, amanufacturer may decide to offer a specified number of transactionincentives to each consumer, and provide to each consumer incentives inthose classes in which it is most likely that the consumer will purchasein the correlated time period, and depend the value of the incentive toeach consumer upon the consumer's likelihood of purchasing from themanufacturer as determined by the consumer's prior purchase history.Alternatively, the manufacturer may decide to provide incentives only toconsumers that previously have consistently purchased from thatmanufacturer in a class but appear unlikely to purchase in that class inthe correlated time period.

Demographic data provides additional information which can be correlatedto consumer's purchases in specified classes. For example, consumerdemographic data may show that consumers having at least one child underthe age of 12 in their household are 70 percent likely to purchase adairy product in each shopping transaction at a supermarket, whereasconsumers without at least one child in their household are only 25percent likely to purchase at least one diary product in each shoppingtransaction at a supermarket. This time independent correlation data maybe combined into the foregoing ranking functions to account for thisknown demographic effect upon a consumers anticipated transactions inthe correlated time period. For example, the R function defined abovemay be modified by adding a fraction corresponding to the demographicbased statistical likelihood of a purchase in the target class. In thatalternative, the function R(P12, n, Sk)=Σ(NCij if Cij is positive −Cijif Cij is negative) could be modified by adding a term Dk where Drepresents the correlation value (0.70 for a consumer who has a childunder 12 and 0.25 for a consumer who does not have a child under age 12)and k represents the specific consumer. Similarly, any other dataproviding a statistical correlation to certain consumer transactions maybe included in a ranking or probability function indicating likelihoodor relative likelihood of a specific consumer transacting in a class ina time correlated period. For example, a retailer loyalty quotient, Q,which is an estimate of the fraction of a consumer's total groceryshopping dollars spent at a certain retailer, may be included in aranking function. For example the value of Qk for consumer k can beadded to the definition of the ranking function such that R(P12, n,Sk)=Σ(NCij if Cij is positive −Cij if Cij is negative)+Dk+Qk.

Additional time independent consumer specific data which may be includedin the ranking of consumers includes a measure of the consumer'slikelihood to redeem transaction incentives that have been provided tothem in various ways, such as via direct mail, email, at the point ofsale, at kiosks not at the point of sale both in a retail store and outof the retail store, and transaction incentives over the Internet at websites. The time independent data relating to the consumer's likelihoodof redeeming based upon the modality of transmission of the transactionincentive to the consumer may be used in determining which modality touse in providing the transaction incentive to the consumer.

An example of a predictive model is Ps(x1, x2, x3) where Ps is astatistical probability of purchase of canned peas in a later timeperiod, x1, x2, and x3 are measures of purchase of milk, cereal, andpaper goods in a prior time period. For example, the functional form ofPs may be Ps=f1(x1)+f2(x2)+f3(x3) where f1 is a function ranging from 0to ½ and f2 and f3 are both functions ranging from 0 to ¼. For exampleof the functional form of f1, f1 may be zero if x1 indicates no purchaseof milk in the prior time period and ranging up to ½ with increasingquantity of milk purchased in the prior time period up to 2 gallons ofmilk. For example of the functional form of f2, f2 may be ¼ if x2 iszero, and x2 may decrease with increasing x2 until x2 reflects purchaseof at least 5 dollars of cereal in the prior time period. For example ofthe functional form of f3, f3 may be zero when the value of x indicatesno purchase of paper goods in the prior time period, and f3 may range upto ¼ as the value of x3 may increase polynomially to indicate purchaseof $20.00 in paper goods.

Another example of a predictive model is Ps(x1, x2, x3) in which Ps isthe probability of a change in purchase behavior of the specifiedproduct in the earlier and later time periods. For example, Ps may bethe probability that quantity of the specified product will decrease,remain constant, or increase. Ps may or may not be a function of ameasure of purchase of the specified quantity in the prior time period.As a specific example, Ps may be the probability of the quantity ofpurchase of canned peas in the second time period being no more than 50percent the quantity of canned peas purchased in the first time period.Again, one possible predictive model is Ps=f1(x1)+f2(x2)+f3(x3).Obviously, the number of variables, x1, x2, and x3 may vary. Preferably,the model includes at least two variables, more preferably three or morevariables. The number of variables, x, in a predictive model of theinvention may range from 2 to over 100, but preferably, in view ofcurrent digital processing limitations, is in the range of 3 to 15, morepreferably 5 to 10. The functional form of Ps, the predictive model, mayalso vary greatly. However, the functional form of Ps should reflect theunderlying correlation of the variables x1, x2, etc with the change inpurchase quantity in the earlier and later time periods of the specifiedproduct. The functional form of Ps may be predetermined by the user orautomatically determined by computer code generating the predictivemodels applicable to each specified product.

This invention will allow a company with a database containing detailedshopping records for numerous consumers to help a cold cerealmanufacturer protect its sales by providing an incentive to a consumerwho has purchased a specific product of that manufacturer's cereal inthe recent past but might stop purchasing the cereal product(hereinafter in this example referred to as “cereal”) in the future. Inorder to accomplish this goal, all of the consumers' purchasing recordsare read into a computer analysis and modeling program which uses one ora combination of regression analysis, neural network analysis, anddecision tree analysis to identify characteristics in the data recordsthat correlate with the subsequent purchase and non-purchase of cerealand to formulate a predictive model based upon the identifiedcorrelations. In formulating a prediction of a specific consumer'spurchases, the modeling program relies on the predictive model generatedby an analysis of statistical population of data records (CID records)applied to actual data in that specific consumer's purchasing records.The purpose of the modeling program is to examine leading indicators anddetermine which consumers are going to change their spending habits andin what way. For example, there may be 30 or 40 products that are highlycorrelated to the future purchase or non-purchase of cereal. Some of theproducts could be complimentary products, such as milk, bananas, orsugar. Other products might be competing products, such as oatmeal,instant breakfast drinks, muffins, or a competitor's brand of cereal.Still other products might be considered which would appear to have no aprior or readily discernable rational reason for correlation to the saleof cold cereal, such as Pez candy or nail polish. The prior purchase ofcereal, the target class, may or may not be one of the variables thatcomprises the model.

In addition to products, modeling may use a wide variety of demographicdata to develop correlations based on indication of a change inlifestyle. Some demographic reasons a consumer would alter theconsumer's consumption of cereal include: an only-child graduating highschool and leaving the household (decreased demand compared to the samehousehold) or the presence of a large number of children under the ageof 15 in a household (increased demand for cereal compared to theaverage household). Note that the preceding two examples that the modelcan accommodate are comparisons between the household at two differenttimes (the child going away to college) and a comparison between thehousehold and other households generally (the household with a largenumber of children under the age of 15). Lifestyle factors may also beincluded in the analysis, such as a job change necessitating a longercommute which did not allow the consumer time to sit down and eatbreakfast, the consumer may have started a fad diet that consisted onlyof meat (eliminating cereal from the foods consumers would eat), or theconsumer's refrigerator may have broken and the consumer was unable tokeep cold milk in the home. Other factors that may indicate that theconsumer might stop purchasing cereal are related to the consumer'sshopping habits. If the consumer constantly changed the type of cerealthe consumer purchased, it might indicate the consumer did not find atype of cereal to the consumer's liking and would make the consumer morelikely to stop purchasing cereal than a consumer that habitually boughta certain brand and product. Such a non-loyal consumer is a prime targetfor an incentive for cereal. A decline in the consumer's frequency ofpurchasing cereal or a decrease in the size of the cereal box purchased,or an increase in the price of cereal might forestall a future declinein the consumer's cereal purchases.

In addition to identifying products which correlate to the non-purchaseof cereal, system 20's analysis formulates the relative weighting ofeach variable and the relevant time period or time periods that provideimproved relatively larger correlations. System 20 may be programmed toevaluate purchases from two or more prior time periods and from timeperiods of different durations in generating a predictive model.Alternatively, system 20 may be limited to a program based on only twotime periods of equal duration. For example, system 20 mightpredicatively model consumer behavior from each one of a subsequent 6months based upon consumer transaction information for the month ofSeptember.

As stated above, all of the variables can be derived by the computerprogram based on a combination of regression analysis, neural networkanalysis, and decision tree analysis. However, if the user chooses, anyvariable may be established by the user.

In a preferred embodiment, after system 20 defined the predictive modelfunction, it applies the predictive model function to a CID record foreach consumer in a set of consumers resulting in a score for each CIDrecord for the targeted behavior. The score is preferably then comparedto a value to determine if the consumer associated with the CID willreceive an incentive. For example, the model for the cereal incentiveprogram introduced above might contain the following product variables:the purchase of milk, napkins, limes, 35 millimeter film, bananas,frozen pizza, oatmeal, sugar, coffee, tissues, and canned corn; as wellas the following demographic data: household income and number ofchildren under the age of 16. Each variable is evaluated to a number.The values of the numbers may be discrete (for example, a “one” for thepurchase of the product during the applicable period and a “zero” forthe absence of a purchase of the product during the relevant time) orcontinuous. These values may be summed to produce a score for each CID.The score need not be an estimate of probability, per se. The modelcould equate a percentile to a score (for example, a score of sevenindicates that seventy percent of the consumers will purchase cerealduring the relevant period and a score of eight might indicate thateighty-five percent of the consumers will purchase cereal during therelevant period). This would enhance decision making regarding whichconsumers to provide an incentive to, for which product, and howenticing the incentive will be. The focus of the scoring could also beto indicate that the consumer was about to stop purchasing cereal, thatthe consumer was about to purchase less cereal, the consumer was aboutto purchase a different brand of cereal, and that the consumer was goingto purchase a hot cereal instead of a cold cereal.

My invention is not limited to the specific example above. It is moreproperly defined by the scope of protection I claim in the followingclaims.

1. A computer system, comprising: a digital processor, memory, a devicefor inputting information from a user, and an output for outputtinginformation; wherein said computer system stores in said memory, datadefining: (1) values representing statistical correlations; (2) a firstplurality of consumer records, each including transaction dataindicating identification of product purchased and date of purchase; (3)a predictive model function; and (4) a second consumer record for asecond consumer including transaction data and a first correlated classpredictive data field; wherein said computer system is programmed todetermine said values representing statistical correlations from saidfirst plurality of consumer records by, first, determining valuesrepresenting correlations of data within each one of said firstplurality of consumer records between (a) and (b), wherein: (a) aretransactions in at least one transaction class for transactions thatoccurred during at least one first time period and (b) are transactionsin at least one transaction class that occurred during at least onesecond time period, said at least one second time period beingsubsequent in time to said at least one first time period, and then,second, determining said values representing statistical correlationsfrom said values representing correlations; wherein said predictivemodel function is defined at least in part by said values representingstatistical correlations; wherein said computer system is programmed toapply said predictive model function to transaction data of said secondconsumer record for transactions that occurred during a third timeperiod, to result in first correlated class predictive data; whereinsaid computer system is programmed to store said first correlated classpredictive data in said first correlated class predictive data field ofsaid second consumer record.
 2. The system of claim 1 wherein saiddetermining values representing correlations does not includedetermining cross-correlations.
 3. The system of claim 1 wherein saiddetermining values representing correlations does include determiningcross-correlations.
 4. The system of claim 1 wherein said computersystem is programmed to determine at least one value representingcorrelations between different transaction classes.
 5. The system ofclaim 1 wherein said computer system is programmed to determine at leastone value representing correlations between all data from a genus oftransaction classes and all data associated with one specie of thatgenus.
 6. The system of claim 1 programmed to determine whether to offera transaction incentive to said second consumer based upon data in saidfirst correlated class predictive data field of said second consumerrecord.
 7. A computer implemented method for using a computer systemcomprising a digital processor, memory, a device for inputtinginformation from a user, and an output for outputting information,comprising: storing in said memory data defining: (1) valuesrepresenting statistical correlations; (2) a first plurality of consumerrecords, each including transaction data indicating identification ofproduct purchased and date of purchase; (3) a predictive model function;and (4) a second consumer record for a second consumer includingtransaction data and a first correlated class predictive data field;determining, using said computer system, values representing statisticalcorrelations from said first plurality of consumer records by, first,determining values representing correlations of data within each one ofsaid first plurality of consumer records between (a) and (b), wherein:(a) are transactions in at least one transaction class for transactionsthat occurred during at least one first time period and (b) aretransactions in at least one transaction class that occurred during atleast one second time period, said at least one second time period beingsubsequent in time to said at least one first time period, and then,second, determining said values representing statistical correlationsfrom said values representing correlations; wherein said predictivemodel function is defined at least in part by said values representingstatistical correlations; applying, using said computer system, saidpredictive model function to transaction data of said second consumerrecord for transactions that occurred during a third time period, toresult in first correlated class predictive data; storing, in saidmemory of said computer system, said first correlated class predictivedata in said first correlated class predictive data field of said secondconsumer record.
 8. A non transitory computer readable medium containinga computer program product for predictive modeling, the computer programproduct comprising: program code for storing in memory of a computersystem, data defining: (1) values representing statistical correlations;(2) a first plurality of consumer records, each including transactiondata indicating identification of product purchased and date ofpurchase; (3) a predictive model function; and (4) a second consumerrecord for a second consumer including transaction data and a firstcorrelated class predictive data field; program code for determining,using said computer system, values representing statistical correlationsfrom said first plurality of consumer records by, first, determiningvalues representing correlations of data within each one of said firstplurality of consumer records between (a) and (b), wherein: (a) aretransactions in at least one transaction class for transactions thatoccurred during at least one first time period and (b) are transactionsin at least one transaction class that occurred during at least onesecond time period, said at least one second time period beingsubsequent in time to said at least one first time period, and then,second, determining said values representing statistical correlationsfrom said values representing correlations; wherein said predictivemodel function is defined at least in part by said values representingstatistical correlations; program code for applying said predictivemodel function to transaction data of said second consumer record fortransactions that occurred during a third time period, to result infirst correlated class predictive data; program code for storing in saidmemory said first correlated class predictive data in said firstcorrelated class predictive data field of said second consumer record.